Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Fitting coarse-grained models to macroscopic experimental data via automatic differentiation.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

What does it take to learn the rules of RNA base pairing? A lot less than you may think.

Communications biology·2026

Same author

Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.

RNA (New York, N.Y.)·2026

Same author

Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods.

bioRxiv : the preprint server for biology·2025

Same author

Generalized design of sequence-ensemble-function relationships for intrinsically disordered proteins.

Nature computational science·2025

Same author

What does it take to learn the rules of RNA base pairing? A lot less than you may think.

bioRxiv : the preprint server for biology·2025

Same journal

Correction to 'New origin firing is inhibited by APC/CCdh1 activation in S-phase after severe replication stress'.

Nucleic acids research·2026

Same journal

VeloRM: disentangling pre- and post-splicing RNA modification dynamics at single-cell resolution.

Nucleic acids research·2026

Same journal

Accessibility of telomeric overhangs to stabilizing small-molecule ligands.

Nucleic acids research·2026

Same journal

Multivalent interactions mediate SNAIL transcription factor stimulation of the nucleosome deacetylase activity of the CoREST complex.

Nucleic acids research·2026

Same journal

Genome-wide mapping of DNA G-quadruplexes in Trypanosoma brucei chromatin reveals enrichment in coding regions and transcription start sites.

Nucleic acids research·2026

Same journal

Correction to 'The Gene Ontology knowledgebase in 2026'.

Nucleic acids research·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 9, 2025

Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA

Practical Aspects of Sample Preparation and Setup of ¹H R_1ρ Relaxation Dispersion Experiments of RNA

Published on: July 9, 2021

Differentiable partition function calculation for RNA.

Marco C Matthies¹, Ryan Krueger², Andrew E Torda¹

¹Centre for Bioinformatics, University of Hamburg, Bundesstr. 43, 20146 Hamburg, Germany.

Nucleic Acids Research

|December 1, 2023

Summary

This summary is machine-generated.

Researchers developed a differentiable RNA partition function calculation. This innovation allows direct gradient optimization for inverse folding, enabling sequences to fold into desired structures.

More Related Videos

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Published on: July 10, 2020

Related Experiment Videos

Last Updated: Jul 9, 2025

Practical Aspects of Sample Preparation and Setup of 1H R1ρ Relaxation Dispersion Experiments of RNA

Practical Aspects of Sample Preparation and Setup of ¹H R_1ρ Relaxation Dispersion Experiments of RNA

Published on: July 9, 2021

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

2D-HELS MS Seq: A General LC-MS-Based Method for Direct and de novo Sequencing of RNA Mixtures with Different Nucleotide Modifications

Published on: July 10, 2020

Area of Science:

Computational Biology
Biophysics
Molecular Biology

Background:

Ribonucleic acid (RNA) plays crucial roles in biological functions.
McCaskill's dynamic programming algorithm (1990) is standard for RNA partition function and thermodynamic analysis.
Current methods lack differentiability for integration with machine learning.

Purpose of the Study:

To generalize McCaskill's algorithm for continuous inputs, enabling differentiable partition function calculations.
To bridge RNA thermodynamics with differentiable programming and deep learning.
To apply the new method to solve the inverse folding problem.

Main Methods:

Generalization of McCaskill's algorithm for continuous inputs.
Implementation of an end-to-end differentiable partition function calculation.
Gradient optimization applied to the inverse folding problem.

Main Results:

The generalized algorithm allows computation of derivatives with respect to input sequences or energy model parameters.
Direct gradient optimization of RNA sequences was sufficient to achieve desired folding structures.
The computed gradients proved meaningful for inverse folding.

Conclusions:

The developed differentiable partition function is a significant advancement for RNA thermodynamics.
This method integrates RNA folding with deep learning pipelines.
The approach effectively solves the inverse folding problem, paving the way for designing RNA sequences with specific structures.